Pearson Distribution

Evaluate Pearson distribution probability functions and generate random samples

Statistics and Machine Learning Toolbox™ offers two ways to work with the Pearson distribution:

Use distribution-specific functions (pearspdf, pearscdf, pearsrnd,pearsinv) with specified distribution parameters. The distribution-specific functions can accept parameters from multiple Pearson distributions.
Use generic distribution functions (cdf, pdf, random,icdf) with the distribution name "Pearson" and specified distribution parameters.

To learn about the Pearson distribution, see Pearson Distribution.

Objects

PearsonDistribution Pearson probability distribution object (Since R2025a)

makedist Create probability distribution object

`cdf`	Cumulative distribution function
`icdf`	Inverse cumulative distribution function
`iqr`	Interquartile range of probability distribution
`mean`	Mean of probability distribution
`median`	Median of probability distribution
`pdf`	Probability density function
`plot`	Plot probability distribution object (Since R2022b)
`random`	Random numbers
`std`	Standard deviation of probability distribution
`truncate`	Truncate probability distribution object
`var`	Variance of probability distribution

`pearspdf`	Pearson probability density function (Since R2023b)
`pearscdf`	Pearson cumulative distribution function (Since R2023b)
`pearsrnd`	Pearson system random numbers
`pearsinv`	Pearson inverse cumulative distribution function (Since R2025a)

Pearson Distribution
The Pearson distribution is a flexible, four-parameter distribution that has an arbitrary mean, standard deviation, skewness, and kurtosis.
Generating Data Using Flexible Families of Distributions
The Pearson and Johnson systems are flexible parametric families of distributions that provide good matches for a wide range of data shapes.